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A time-frequency channel attention and vectorization network for automatic depression level prediction | |
Mingyue Niu1,2![]() ![]() ![]() | |
发表期刊 | Neurocomputing
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2021 | |
期号 | 450页码:208-218 |
摘要 | Physiological studies have illustrated that speech can be used as a biomarker to analyze the severity of depression and different frequency bands of the speech spectrum contribute unequally for depression detection. To this end, we propose a Time-Frequency Attention (TFA) component and combine it with the Squeeze-and-Excitation (SE) component to form our Time-Frequency Channel Attention (TFCA) block for emphasizing those discriminative timestamps, frequency bands and channels. In addition, considering the time-frequency attributes of the data, a Time-Frequency Channel Vectorization (TFCV) block is proposed to vectorize the tensor. Furthermore, we merge the proposed blocks (i.e., TFCA and TFCV blocks) and the two blocks (i.e., Dense block and Transition Layer) of the DenseNet into a unified architecture to form our Time-Frequency Channel Attention and Vectorization (TFCAV) network. In this way, to predict the depression level of an individual, we firstly introduce the sphere embedding normalization method to preprocess the long-term logarithmic amplitude spectrum for maintaining the time-frequency attributes and divide it into segments. Then, these segments are input into the TFCAV network to obtain the depression scores. Finally, the average of scores is taken as the result corresponding to the long-term spectrum. Our method is validated on two challenging databases, i.e., AVEC2013 and AVEC2014 depression databases. The experimental performance illustrates the superiority of the proposed network over some previous methods. |
关键词 | Sphere embedding normalization DenseNet Transition layer Time-frequency channel attention block Time-frequency vectorization block Depression detection |
收录类别 | SCI |
七大方向——子方向分类 | 多模态智能 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44398 |
专题 | 多模态人工智能系统全国重点实验室_智能交互 |
通讯作者 | Bin Liu; Jianhua Tao |
作者单位 | 1.National Laboratory of Pattern Recognition, CASIA, Beijing, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 3.CAS Center for Excellence in Brain Science and Intelligence Technology, Beijing, China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Mingyue Niu,Bin Liu,Jianhua Tao,et al. A time-frequency channel attention and vectorization network for automatic depression level prediction[J]. Neurocomputing,2021(450):208-218. |
APA | Mingyue Niu,Bin Liu,Jianhua Tao,&Qifei Li.(2021).A time-frequency channel attention and vectorization network for automatic depression level prediction.Neurocomputing(450),208-218. |
MLA | Mingyue Niu,et al."A time-frequency channel attention and vectorization network for automatic depression level prediction".Neurocomputing .450(2021):208-218. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
A Time-Frequency Cha(2001KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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